Top Data & Analytics Jobs

Design and implement enterprise data architectures and cloud data foundations for financial-services transformation projects. Lead data modeling, integration, ETL/ELT, governance, source onboarding, legacy migration, and architecture workstreams using Azure Databricks. Collaborate with business, engineering, governance, and operations teams while documenting solutions, guiding engineers, and transferring capabilities into production operations.

Design and implement enterprise data architectures for regulated financial-services clients. Build Azure Databricks cloud foundations, onboard data sources, develop enterprise models, establish ETL/ELT and governance patterns, guide legacy migrations, lead architecture workstreams, and collaborate with business, engineering, governance, and operations teams. Support AI, analytics, reporting, documentation, and knowledge transfer into operations.

Design and implement enterprise data architectures for regulated financial-services clients. Build Azure Databricks cloud data foundations, onboard sources, develop enterprise data models, and establish ETL, ELT, API, governance, security, metadata, lineage, and data-quality patterns. Lead architecture decisions, migration workstreams, and project teams while collaborating with business, engineering, governance, and operations stakeholders. Support documentation, knowledge transfer, and transition into operations.

Design, build, and optimize Snowflake-based data platforms and ELT/ETL pipelines for financial-services clients. The role involves data modeling, governance, performance and cost optimization, cloud engineering, stakeholder collaboration, solution design, technical leadership, mentoring, and client engagement. Candidates need strong Snowflake expertise, modern data engineering skills, consulting ability, and experience leading technical workstreams in Agile environments.

Design and develop data models, transformations, hubs, lakes, reporting platforms, and warehouses for insurance and reinsurance clients. Analyze business requirements, lead solution development, manage projects or subprojects, and act as a primary client contact. The role requires expertise in data engineering or analytics, insurance data models, stakeholder communication, Agile and DevOps methodologies, and leadership of internal and client teams.

Design and develop data models, transformations, data hubs, lakes, reporting platforms, and warehouses for insurance and reinsurance clients. Analyze business requirements, lead data solution projects, manage client and internal teams, and serve as a subject-matter expert and primary client contact. The role requires insurance data knowledge, stakeholder communication, project ownership, and German and English fluency.

Design and develop data models, transformations, and data platforms for insurance and reinsurance clients. Analyze business requirements, define solution approaches, lead data solution projects, and manage internal and client teams. Serve as a subject-matter expert and primary client contact while supporting migrations and redesigns of insurance business systems. Apply data engineering, analytics, DevOps, Agile, and stakeholder-management expertise.

Design and develop data solutions for insurance and reinsurance clients, including data models, transformations, data hubs, data lakes, reporting platforms, and warehouses. Analyze business requirements, lead solution design, manage project or subject-matter responsibilities, and serve as a primary client contact. Lead internal and client teams while applying data engineering, analytics, DevOps, and Agile practices.

Lead the architecture and design of a scalable data lakehouse, including data models, data flows, ingestion patterns, governance, security, lineage, migration, and integration. Provide technical leadership to data engineering teams and support solution reviews, testing, production readiness, and handover. The role requires strong Azure, Databricks, Delta Lake, data integration, data governance, and cloud architecture expertise, preferably in financial services or another regulated industry.

Develop reusable batch, CDC, and streaming data ingestion and transformation pipelines for enterprise Data Lake and Lakehouse programs. Build Bronze, Silver, and Gold data layers; implement data quality, reconciliation, validation, monitoring, and governed data products. Support migration, integration testing, UAT, production releases, and BAU handover while following cloud, security, governance, and engineering standards. Collaborate with architects, analysts, BI developers, and client technology teams in financial services transformation projects.